7results about How to "Reduce contribution" patented technology

The invention discloses a gastrointestinal symptom identification method and a sensing system, and relates to the technical field of health informatics and medical information processing, and the method comprises the steps: collecting excitation response and abdominal sound to calculate an acoustic transfer function, and employing a main formant frequency to invert a quality loading index representing a condensation water film effect; constructing an equalization gain based on the transfer function to carry out spectral shape correction on the abdominal sound time-frequency spectrum; constructing a symptom evidence vector and performing adaptive weighted scaling on the symptom evidence vector by using a quality loading index; and finally calculating the distance between the weighted vector and the symptom mode prototype library and mapping the distance into a prompt probability. According to the method, the problems of acoustic link drift and signal distortion caused by microenvironment humidity change in long-time monitoring are solved, and the accuracy and robustness of symptom identification are improved.

This invention discloses an online method and system for estimating the braking clearance throughout the entire lifecycle of an EMB system, relating to the field of braking technology for EMB systems in autonomous vehicles. The invention includes: acquiring motor current and motor angle signals of the electromechanical braking system during the braking clamping phase; constructing a current-angle regression model based on a Legendre orthogonal basis based on the nonlinear relationship between the motor current and motor angle signals; performing online robust estimation of the parameter vector of the current-angle regression model using a weighted multi-forgetting factor recursive least squares algorithm combined with an adaptive M-estimator and a two-stage estimation strategy; and mapping the parameter estimates to braking clearance estimates. This invention achieves high-precision braking clearance estimation without additional sensors based solely on motor current and angle signals through online self-learning. It maintains excellent robustness and stability under complex operating conditions and long-term operation, making it suitable for engineering applications in EMB systems.

This disclosure describes a sensing capability resistant to crosstalk. A current sensor is provided for a target conductor among a plurality of conductors. The current sensor includes at least one magnetic sensor configured to provide two signals representing two different parameters of a field, the two different parameters being different components or their directional derivatives (e.g., gradients). The current sensor also includes a processor configured to derive a signal indicating a current based on a linear combination of a first signal and at least a second signal. At least one of these signals is weighted by coefficients that are constants selected based on the distance between the sensor and at least one of the plurality of conductors in at least a first or second direction. The coefficients are selected to reduce the contribution of parasitic magnetic fields to the signal indicating a current in the first conductor, wherein the parasitic magnetic field is generated by at least one other conductor.

The application discloses a kind of based on LoRa's sea cable state remote wireless telemetry system and method, belong to cable laying state monitoring technical field, system includes being laid on several state monitoring nodes of sea cable, relay node and shipborne monitoring center.Adaptive different compensation strategies are taken according to the proportion of missing packet loss monitored by analysis computer: when mild packet loss, trend filling is executed using effective node change rate and spatial correlation;When moderate packet loss, confidence weight that attenuates dynamically with interruption time is introduced in reconstruction model;When severe packet loss, spatial physical correlation constraint matrix feature sub-block is extracted to perform order reduction calculation and combine spatial interpolation method to recover data.The application solves the problem of data loss caused by unstable wireless communication in extreme sea conditions through hierarchical compensation mechanism, ensuring that the full-line state distribution of sea cable can be reconstructed stably and accurately and the warning can be output under different packet loss conditions, significantly improving the robustness and construction safety of the sea cable monitoring system.

A multimodal brain disease diagnostic system based on phenotypic priors and dual-spectral-domain synergistic enhancement. This system belongs to the interdisciplinary field of artificial intelligence and brain science. It addresses the technical problem that existing methods struggle to meet practical needs in terms of diagnostic accuracy and model robustness when dealing with highly heterogeneous multicenter clinical data. The system described in this invention preserves the spectral heterogeneity of rs-fMRI signals to enhance feature expression; constructs a more reliable population graph through diagnostic conflict suppression; aligns cross-modal representations using contrastive learning and gating mechanisms; and captures multi-scale population structure by combining spectral-domain graph filtering, thereby significantly improving the accuracy, robustness, and interpretability of brain disease diagnosis.

This invention discloses a method for low-altitude UAV target identification and early warning based on multi-source perception, relating to the field of low-altitude security and control technology. The method includes: acquiring low-altitude perception data from radar, photoelectric, and passive radio frequency sensors, and preprocessing it to form standardized multi-source observation data; establishing a source quality assessment model to calculate source quality factors and performing spatiotemporal alignment of the data; outputting the target fusion trajectory state quantity and confidence level through gated probabilistic correlation and adaptive weighted fusion, and extracting multi-dimensional features such as radar micro-Doppler, photoelectric shape, and radio frequency fingerprint to construct a fusion feature vector input classifier to identify target categories; establishing a threat scoring function by combining trajectory prediction and behavioral pattern analysis, outputting threat level and confidence level, and executing graded early warning based on dynamic thresholds. This invention solves the problems of unstable detection by a single sensor, low identification accuracy, and delayed early warning in low-altitude environments, improving the robustness and predictability of low-altitude UAV target control.

A method for predicting coal spontaneous combustion temperature based on adaptive fusion of multi-source heterogeneous data is proposed. This method collects four data sources: time-series data of indicator gas concentrations, distributed fiber optic temperature field data, infrared thermal imaging data, and environmental parameter data. Modal features are extracted using bidirectional long short-term memory networks, one-dimensional convolutional networks, two-dimensional convolutional networks, and fully connected networks, respectively. Data quality scores are calculated in real-time for each data source, generating adaptive fusion weights. A cross-modal multi-head attention mechanism is used to deeply fuse the multi-modal features. The fused features are then propagated multiple times forward using a Monte Carlo random deactivation method, outputting the predicted temperature value and its confidence interval. A risk assessment index is constructed by combining the temperature rise rate, enabling graded early warning of coal spontaneous combustion temperature. This invention overcomes the shortcomings of existing methods, such as reliance on a single data source, lack of sensor fault tolerance, and lack of confidence assessment for prediction results, thus improving the accuracy, robustness, and scientific validity of coal spontaneous combustion temperature prediction and early warning.